307 research outputs found
A 3-D vortex-boundary element method for the simulation of unsteady, high Reynolds number flows
No full textThesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1995.Includes bibliographical references (p. 266-271).by Adrin Gharakhani.Sc.D
Does financial development reduce CO2 emissions in Malaysian economy? / Irdina Adrin
No full textIn the country of Malaysia, this study deals with the question whether financial development reduces C02 emissions or not in case of Malaysia. The limits testing technique to cointegration between variables is used for this purpose. We show that C02 emissions, financial development, economic growth, foreign direct investment, and trade all have strong long-term correlations. Financial development also appears to lower C02 emissions, according to the findings. C02 emissions are exacerbated by increased energy usage and economic growth. The latest research paper found with similar executions results that the Granger causality analysis demonstrates the feedback hypothesis between financial development and C02 emissions, as well as between C02 emissions and economic growth was in 2013. There is no similar research paper that is more updated to this current year, however in 2020, there is a paper that conducted similarly which examines the impacts of financial development on sectoral carbon emissions (C02) for environmental quality in Malaysia. Conclude that in general, financial development increases C02 emissions and reduces environmental quality in Malaysia. The dependant variable for this research study is C02 Emissions while the independent variables are financial development, economic growth, foreign direct investment, and trade. This study will be from the year 1971 to this recent year of 2020. The variables examined in this study (Financial Development, Economic Growth, Foreign Direct Investment and Trade) showed a similar linkage on the C02 Emissions that has been explored in previous research. However, there is only one independent variable that has a significant impact on the C02 Emissions, and that is the Financial Development, which has a positive relationship. Others, such as Economic Growth and Foreign Direct Investment, have a negative and insignificant influence on the C02 Emissions, whereas Trade has a positive and insignificant impac
eran Keluarga dalam Pemberian Nutrisi pada Balita di Puskesmas Pasayangan Martapura Kalimantan Selatan
No full textProgram Studi Ilmu Keperawatan
Fakultas Kedokteran
Universitas Diponegoro Semarang
Januari 2008
ABSTRAK
Adrin Afriyanti
Peran Keluarga dalam Pemberian Nutrisi pada Balita di Puskesmas Pasayangan Martapura Kalimantan Selatan
xii + 52 halaman + 1 gambar + 26 lampiran
Peranan keluarga menggambarkan seperangkat perilaku interpersonal, sifat, kegiatan yang berhubungan dengan individu dalam posisi dan situasi tertentu.
Penelitian ini menggunakan metode kualitatif dengan pendekatan etnografi yang bertujuan untuk mengetahui peran keluarga dalam pemberian nutrisi pada balita. Teknik pengumpulan data dengan wawancara mendalam dengan sampel pada keluarga inti (bapak, ibu, anak) yang di wawancara bapak atau ibu yang memiliki anak balita berjumlah 5 orang.
Penelitian menunjukkan peran orang tua terhadap pemenuhan nutrisi beragam, namun mereka hanya mementingkan dari segi keuangan, makanan, kesehatan. Aspek penting yang lain hanya sebagai pendukung. Orang tua menyadari betapa pentingnya nutrisi namun secara perilaku mereka kurang begitu peduli terhadap nutrisi yang cocok dikonsumsi oleh balita. Faktor yang menghambat seperti keuangan serta ketelatenan dari orang tua.
Hasil penelitian perlu ditindak lanjuti untuk mengetahui apa kebiasaan masyarakat masih saja melekat serta tingat kepedulian orang tua seberapa besar terhadap pemenuhan nutrsi balita, jika terjdi perubahan maka seberapa besar perubahan itu terjadi.
Kata kunci : Peran, Pertumbuhan, kebutuhan nutrisi, Masalah terjadi, cara mengatasi masalah
Attention-Driven Recurrent Imputation for Traffic Speed
No full textIn practice, traffic data collection is often warned by missing data due to communication errors, sensor failures, storage loss, among other factors, leading to impaired data collection and hampering the effectiveness of downstream applications. However, existing imputation approaches focus exclusively on estimating the lost value from incomplete observations and ignore historical data. In this paper, we propose a novel neural network model, namely, Attention-Driven Recurrent Imputation Network (ADRIN), to address the problem of missing traffic data. Specifically, in ADRIN, we devise an Imputation-targeted Long Short-Term Memory (LSTM-I) module for filling in missing data. Meanwhile, we consider the periodicity of historical data and design a historical average calculation module in ADRIN. On this basis, we employ the multi-head self-attention mechanism for further extracting latent temporal features from the output of the two modules. ADRIN is capable of modeling both incomplete observation inputs and historical averages independently to estimate the missing values. We conducted comprehensive experiments on three real-world traffic datasets, to demonstrate that ADRIN consistently outperforms other baselines in a variety of scenarios. Furthermore, ablation experiments are conducted on the various modules of the model, and it is concluded that historical data can significantly enhance the imputation effect
Attention-Driven Recurrent Imputation for Traffic Speed
No full textIn practice, traffic data collection is often warned by missing data due to communication errors, sensor failures, storage loss, among other factors, leading to impaired data collection and hampering the effectiveness of downstream applications. However, existing imputation approaches focus exclusively on estimating the lost value from incomplete observations and ignore historical data. In this paper, we propose a novel neural network model, namely, Attention-Driven Recurrent Imputation Network (ADRIN), to address the problem of missing traffic data. Specifically, in ADRIN, we devise an Imputation-targeted Long Short-Term Memory (LSTM-I) module for filling in missing data. Meanwhile, we consider the periodicity of historical data and design a historical average calculation module in ADRIN. On this basis, we employ the multi-head self-attention mechanism for further extracting latent temporal features from the output of the two modules. ADRIN is capable of modeling both incomplete observation inputs and historical averages independently to estimate the missing values. We conducted comprehensive experiments on three real-world traffic datasets, to demonstrate that ADRIN consistently outperforms other baselines in a variety of scenarios. Furthermore, ablation experiments are conducted on the various modules of the model, and it is concluded that historical data can significantly enhance the imputation effect
Adrin Cristal
No full textModern out-of-order processors tolerate longlatency memory operations by supporting a large number of inflight instructions. This is achieved in part through proper sizing of critical resources, such as register files or instruction queues. In light of the increasing gap between processor speed and memory latency, tolerating upcoming latencies in this way would require impractical sizes of such critical resources
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